The present invention generally relates to the field of feature selection, and more particularly relates to selecting features for linear regression.
Feature selection methods are critical for classification and regression problems. For example, it is common in large-scale learning applications, especially for biology data such as gene expression data and genotype data, that the amount of variables far exceeds the number of samples. The “curse of dimensionality” problem not only affects the computational efficiency of the learning algorithms, but also leads to poor performance of these algorithms. To address this problem, various feature selection methods can be utilized where a subset of important features is selected and the learning algorithms are trained on these features.